Exploratory Data Analysis with Python.

Real datasets rarely arrive ready to analyze, so the real skill is working them from end to end, from raw file to real-world insight. In this course, explore how to do exactly that with Python and pandas, using a live FAA bird-strike dataset. Learn how to load raw data, handle missing values, and clean messy columns from scratch. Discover how to engineer features with joins and Boolean indicators, run correlation analysis, and visualize distributions to spot and handle outliers. Along the way, explore time-based and geospatial analysis, mapping geographic patterns with GeoPandas and aggregating your findings with group-by and pivot tables. By the end of this course, you'll be ready to run a complete EDA workflow on a real dataset and communicate the patterns you uncover. This intermediate-level course is an ideal fit for data analysts, data scientists, Python developers, and anyone eager to turn raw data into insight.

Learning objectives
Load and inspect a large real-world dataset in pandas.
Clean missing values and cast columns to appropriate data types.
Engineer new features using joins, aggregations, and Boolean indicators.
Analyze relationships with correlation matrices and time-based trends.
Identify and handle outliers in the context of the problem.
Visualize geographic patterns using GeoPandas map layers.